Previous work has been put into the creation of a global soil taxonomy using a harmonised dataset of 23 soil properties at 18 depth intervals. The taxonomy consisted of selected soil taxa from the US Soil Taxonomy, World Reference Base for Soil Resources, the Australian Soil Classification, and the New Zealand Soil Classification. In this paper, a nomenclature algorithm was proposed for this established comprehensive taxonomy. Firstly, a Ward dendrogram was calculated from a weighted distance matrix determined from principal components of the taxa. This dendrogram was then cut at three levels, creating 15 groups, 86 subgroups, and 493 sub-subgroups at tiers 1, 2 and 3, respectively. A sequence of consonants was used to name the taxa at each tier alphabetically with “A” and “E” inserted between the consonants of tiers 2 and 3 and “OZEM” appended after the consonants of tier 1. In addition, a distance-based algorithm was used to allocate and name 10 unknown soil profiles to the comprehensive soil taxonomy. It was concluded that the nomenclature algorithm can be easily disaggregated by computer and can be used to understand the inter-relationships between soil profiles from different classification systems. In the future, there is a need to include other soil classification systems to the comprehensive system and assign different weights to the soil properties and depths used to construct the comprehensive soil classification system.

Soil taxonomies over the world are incongruent- based on different tiers and different sets of properties. This second paper is concerned with understanding the relationships of each tier (Order, Suborder and Great Group) in both Soil Taxonomy (ST) and the Australian Soil Classification system (ASC) using mean nearest neighbour distances and convex hull areas in two principal component dimensions. It is determined that in most instances, convex hull comparisons using only two principal components, representing 30% of the variation in the data describe much of the variability between and within the orders, suborders and great groups of each classification system. These are useful for visual comparisons of taxa at various levels. Mean nearest-neighbour distances can include all 414 variables if necessary, which is more rigorous but complex. Both distance calculations and convex hulls highlight the same associations between taxa from ASC and ST. Both these methods demonstrate the robust soil classification capability of the ST and the ASC, but with convex hull sizes and nearest-neighbour distances that are smaller, the ASC proves to be slightly more coherent. The two systems occupy somewhat different areas in PC space, and ST covers a larger overall area, demonstrating that ASC is a purpose-built classification for Australian conditions while ST Is a more general system that can cover a wider variety of soils and management issues. We also show that great groups in ST are at about the same level of taxonomic generalization as great groups of the ASC. Combining the best elements and taxa of both these systems would be a positive step in the creation of a comprehensive system.

Soil classification as a world exercise consists of predominantly individual organizations, creating locally meaningful categories for regional soils. This process has inevitably created a recognized disconnect between classification systems, and a push for a universal classification has been proposed. In this paper, as a way of standardization between systems, soil taxa at the great group level from two separate regions and soil classification systems, Australia and the United States of America were represented by separate databases of soil profile descriptions (SPDs) comprising the same 23 properties at 18 depth intervals. Taxa centroids from Soil Taxonomy (ST) and the Australian Soil Classification System (ASC) were calculated via principal component analysis. Convex hulls of each soil order of both systems were created and the associations each taxon had with other individuals in the same taxon discussed, as well as the variance. We determined that ASC orders have smaller overall dispersion compared with the ST. The influence of each property to the overall taxonomic distances was also explored. It was concluded that this analysis opened the way for the possibility of comparing differing taxonomies and could pave the way for a more comprehensive classification method.

A new complete map of soil series probabilities has been produced for the contiguous United States at a 30m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30m) elevation data and coarse-scale (~2km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.

The GlobalSoilMap initiative calls for the generation of continuous maps for soil properties, including pH in a 1:5 suspension of soil in water (pH1:5W) based on a standard method, ISO 10390. The United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS) employs a 1:1 suspension of soil in water (pH1:1W), and a 1:2 suspension of soil in CaCl2 (0.01M) (pH1:2CaCl2) for routine pH analysis (Soil Survey Staff, 2009). The objective of this study was to determine the most efficient way to convert these pH values to the GlobalSoilMap standard. For this analysis, 563 soil samples from the USDA-NRCS-National Soil Survey Center (NSSC) soil archive, which had been previously analysed for pH1:1W and pH1:2CaCl2, were selected for determination of pH1:5W, pH1:5CaCl2 and electrical conductivity (EC) in 1:2 suspension of soil in water (EC 1:2W). The samples represented 11 soil orders, 8 mineralogy classes, 5 family particle size classes, 4 genetic master horizons, and 7 depth intervals. For each category, 25–30 samples were selected to represent a comprehensive pH range. Regression analysis showed strong and significant relationships (R2>0.92) between pH methods across all categories. The simple linear regression equation, pH1:5W=−0.51+1.06 pH1:1W, had an RMSE=0.44 pH units. Smoothing spline, did not significantly improve pH1:5W predictions, nor did the incorporation of EC. Genetic horizons and soil depth intervals did not have a significant effect on pH1:5W. The linear regression models for predicting pH1:5W using pH1:1W or pH1:2CaCl2 as predictors emerged as the best candidates for a standard pedotransfer function. Using pedotransfer functions such as these will allow for the simple conversion of existing measured and estimated pH1:1W or pH1:2CaCl2 values from NRCS databases to the GlobalSoilMap standard of pH1:5W.

In September 2009, in Budapest, Hungary, several events were organized to celebrate the 100th anniversary of the first International Conference of Agrogeology. A symposium to review the 100 years of advances in soil sciences and a seminar entitled "From the Dokuchaev School to Numerical Soil Classifications" were organized. As result of these discussions, a resolution (known as the "Godollo Resolution") was prepared and forwarded to the International Union of Soil Science (IUSS) Council for discussion at the 2010 World Congress of Soil Sciences in Brisbane, Australia. The resolution stated that there is a need (i) to develop common standards, methods, and terminology in soil observations and investigations and a universal soil classification (USC) system and (ii) for a new working group to coordinate the efforts of this global undertaking. There was a general agreement that there is a need for evaluation of current spatial soil definition and classification systems and new innovative approaches should be investigated to develop a common universally accepted system. During the 2010 World Congress of Soil Sciences in Brisbane, Australia, the IUSS Council unanimously accepted the Godollo Resolution and formally accepted the proposal for a new working group to carry out the proposed investigations and development of common standards, methods, and terminology in soil observations and investigations and a USC system.

Soils are increasingly recognized as major contributors to ecosystem services such as food production and climate regulation ( 1 , 2 ), and demand for up-to-date and relevant soil information is soaring. But communicating such information among diverse audiences remains challenging because of inconsistent use of technical jargon, and outdated, imprecise methods. Also, spatial resolutions of soil maps for most parts of the world are too low to help with practical land management. While other earth sciences (e.g., climatology, geology) have become more quantitative and have taken advantage of the digital revolution, conventional soil mapping delineates space mostly according to qualitative criteria and renders maps using a series of polygons, which limits resolution. These maps do not adequately express the complexity of soils across a landscape in an easily understandable way.